NPU development guide¶
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Quectel Pi H1 product SoC integrates Qualcomm ® Hexagon ™ Processor (NPU), a hardware accelerator dedicated to inference. To use NPU for model inference, QAIRT (Qualcomm® AI Runtime SDK) is required to port pre-trained models. Qualcomm ® provides a series of SDKs for NPU developers to facilitate users to deploy their models onto the NPU.
Model quantification library: AIMET
Model porting SDK: QAIRT
Model application library: QAI-APP-BUILDER
Online model conversion library: QAI-HUB
Preparations¶
Create Python execution environment¶
sudo apt install python3-numpy
python3.10 -m venv venv
. venv/bin/activate
Download testing program¶
Download npu-inference-sample-yolov8-for-h1, unzip it and enter the directory.
unzip ai-test.zip
cd ai-test
chmod +x *
Execute AI inference¶
Execute the program to load the model and test dataset:
./qnn-net-run --backend ./libQnnHtp.so \
--retrieve_context resnet50_aimet_quantized_6490.bin \
--input_list test_list.txt --output_dir output_bin
View results¶
Execute the script to view the results:
python3 show_resnet50_classifications.py \
--input_list test_list.txt -o output_bin/ \
--labels_file imagenet_classes.txt
Script output result:
Classification results
./images/cock.raw [cock]
./images/goldfish.raw [goldfish]
./images/ice_bear.raw [ice bear]
./images/mouse.raw [mouse]
Test image collection¶
NPU software stack¶
QAIRT¶
QAIRT (Qualcomm ® AI Runtime ) SDK is a software package that integrates Qualcomm ® AI software products, including Qualcomm ® AI Engine Direct, Qualcomm ® Neural Processing SDK and Qualcomm ® Genie. QAIRT provides developers with a complete set of tools for porting and deploying models on Qualcomm® hardware accelerators, as well as the runtime for executing models on CPU, GPU, and NPU.
Supported inference backends:
CPU
GPU
NPU
SoC architecture comparison table
SoC |
dsp_arch |
soc_id |
|---|---|---|
QCS6490 |
v68 |
35 |
QCS9075 |
v73 |
77 |
AIMET¶
AIMET(AI Model Efficiency Toolkit)is a quantization tool for deep learning models such as PyTorch and ONNX. AIMET enhances model runtime performance by reducing computational load and memory footprint. With AIMET, developers can quickly iterate and find the optimal quantization configuration to achieve the best balance between accuracy and latency. Quantization models exported by AIMET can be compiled and deployed to the Qualcomm NPU via QAIRT, or executed directly using ONNX Runtime.
QAI-APPBUILDER¶
Quick AI Application Builder (QAI AppBuilder) assists developers in deploying AI models and designing AI applications on Qualcomm® SoC platforms equipped with the Qualcomm® Hexagon™ Processor (NPU) based on the Qualcomm® AI Runtime SDK. This tool encapsulates the model deployment APIs into a set of simplified interfaces for loading models onto the NPU and executing inference, significantly reducing the complexity of model deployment for developers. Additionally, QAI AppBuilder provides several Demos for reference, helping developers quickly build their own NPU Applications.
QAI-Hub¶
Qualcomm® AI Hub (QAI-Hub) is a one-stop online service platform for model conversion, offering online model compilation, quantization, performance analysis, inference, and download services. It automates the model conversion process from pre-trained model to device runtimes. Among them, Qualcomm® AI Hub Models(QAI-Hub-Models) is a Python library based on QAI-Hub, enabling users to perform model quantization, compilation, inference, analysis, and download through the AI Hub service.